Isolation and quantification of dialkylmercury species by headspace solid phase microextraction and gas chromatography with atomic emission detection.

Foi desenvolvida uma metodologia para quantificar compostos dialquilmerc?ricos usando Microextra??o em Fase S?lida em Headspace (HS-SPME) e Cromatografia Gasosa com Detec??o por Emiss?o At?mica (GC-AED). Os par?metros para detec??o de Hg foram otimizados usando planejamento fatorial e superf?cies de resposta. Experimentos univariados foram empregados para determinar as condi??es de HS-SPME; as melhores fibras foram 75 ?m de Carboxen / PDMS e 65 ?m de PDMS / DVB. Por?m, as primeiras foram descartadas pela extensa degrada??o t?rmica dos analitos na dessor??o. O procedimento otimizado permite detectar os analitos em amostras aquosas com limite de detec??o de 1,7 e 0,2 ng L-1 para dimetil- and dietilmerc?rio, respectivamente. As curvas anal?ticas s?o lineares nas faixas de 36 a 180 ng L-1 (Me2Hg) e 38 a 190 ng L-1 (Et2Hg), com limite de quantifica??o de 38 ng L-1 (Me2Hg) e 29 ng L-1 (Et2Hg) e coeficientes de correla??o de 0,998 para Me2Hg e 0,999 para Et2Hg.A methodology to quantify dialkylmercury compounds using Headspace Solid Phase Microextraction (HS-SPME) and Gas Chromatography with Atomic Emission Detection (GC-AED) was developed. The parameters for Hg detection were optimized by factorial design and response surfaces. Univariate experiments were employed to determine the HS-SPME conditions; 75 ?m Carboxen / PDMS and 65 ?m PDMS / DVB were the best fibers. However, the former was excluded from further experiments due to extensive thermal degradation of analytes during desorption. The optimized procedure allowed detection of the analytes from aqueous samples with LOD of 1.7 ng L-1 and 0.2 ng L-1 for dimethyl- and diethylmercury, respectively. The analytical curves are linear in the range from 36 to 180 ng L-1 (Me2Hg) and 38 to 190 ng L-1 (Et2Hg), with LOQ of 38 ng L-1 (Me2Hg) and 29 ng L-1 (Et2Hg) and correlation coefficients of 0.998 for Me2Hg and 0.999 for Et2Hg

Exploratory analysis of the volatile profile of beers by HS?SPME?GC.

Kohonen Neural Network maps were used for exploratory analysis of Brazilian Pilsner beers. The input data consisted of the peak areas of the volatile profile compounds of samples obtained after headspace solid phase microextraction coupled to gas chromatography. The chromatographic peaks were identified as originating from compounds such as alcohols, esters, organic acids, phenolic compounds, ketone and others typically found in the headspace of such samples. Analysis of the Kohonen maps showed that the 20 different brands of beer could be grouped into six sets, with three of these sets having only one sample, according to the composition of their volatile fractions. The volatile species associated with the similarities and differences between each sample group were tentatively identified by mass spectrometry mand their contributions to the grouping are discussed

Proposition of operational performance indicators system applied for water treatment plants focusing on service provider : application for five conventional water treatment plants.

O principal objetivo desta pesquisa consistiu em desenvolver e aplicar um sistema de indicadores de desempenho direcionado a esta??es convencionais de tratamento de ?gua com base na vis?o do prestador de servi?o. A metodologia abrangeu tr?s etapas principais: (i) defini??o dos indicadores e justificativa; (ii) formula??o e aplica??o do sistema de indicadores a um conjunto de cinco esta??es de pequeno porte (vaz?es nominais de 20 a 60 L.s-?) operadas pelo mesmo prestador; (iii) an?lise estat?stica a partir dos resultados de c?lculo dos indicadores visando identificar eventuais sobreposi??es. O sistema proposto abarcou 13 indicadores de desempenho, calcados em par?metros comumente inseridos na rotina operacional das esta??es de tratamento brasileiras.The main objective of this research was to develop and apply a performance indicator system focusing on conventional water treatment plants, based on the service provider?s point of view. The methodology comprised three principal steps: (i) definition of indicators and justification; (ii) development and application of the indicator system to five small plants (flow rate from 20 to 60 L.s-1) operated for same provider; (iii) statistical analysis of the results, aiming to identify overlapping among the proposed performance indicators. The system comprised 13 performance indicators whose application is based on parameters usually monitored in the vast majority of Brazilian plants

Multivariate analysis applied for study of the sampling frequency and the number of sampling stations in water quality monitoring.

O cerne do presente trabalho consistiu em aplicar ferramentas de an?lise explorat?ria multivariada objetivando avaliar o n?mero de esta??es de monitoramento de qualidade da ?gua e a frequ?ncia de amostragem. Para tal, utilizou-se banco de dados disponibilizado pelo Instituto Mineiro de Gest?o das ?guas (IGAM) referente ? Bacia do Rio das Velhas, na regi?o central mais populosa de Minas Gerais. Foram utilizadas as t?cnicas de an?lise das componentes principais (ACP) e a rede neural de Kohonen, que culminaram na significativa redu??o da frequ?ncia de amostragem, em alguns casos de mensal para anual ou semestral, e na redu??o do n?mero de esta??es de monitoramento de 36 para 33. Os resultados permitem abrir a possibilidade do emprego dos m?todos utilizados como ferramentas de gest?o de recursos h?dricos de bacias hidrogr?ficas visando ? otimiza??o dos programas de monitoramento de qualidade de ?gua.The core of this work consisted of applying multivariate exploratory analysis tools to evaluate the number of water quality monitoring stations and the sampling frequency. In such way, the database provided by the Minas Gerais Institute of Water Management (IGAM) on the river basin of Rio das Velhas, in the most populous central region of Minas Gerais state, was used. The Principal Components Analysis and the Kohonen neural network techniques were applied, resulting in a significant reduction in sampling frequency, in some cases from monthly to annual or semi-annual, and in the reduction of the number of monitoring stations from 36 to 33. The results open the possibility of using these methods as watershed water resources management tools aimed at optimization of water quality monitoring programs

Application of Kohonen neural network for evaluation of the contamination of Brazilian breast milk with polychlorinated biphenyls.

Due to the tendency of polychlorinated biphenyls (PCB) to accumulate in matrixes with high lipid content, the contamination of the breast milk with these compounds is a serious issue, mainly to the newborn. In this study, milk samples were collected from breastfeeding mothers belonging to 4 Brazilian regions (south, southeast, northeast and north). Twelve PCB were analyzed by HS-SPME-GC-ECD and the corresponding peak areas were correlated to the answers to a questionnaire of general habits, breastfeed- ing and characteristics of the living places. To realize this exploratory analyze, self-organizing maps generated applying Kohonen neural network were applied. It was possible to verify the occurrence of different PCB congeners in the breast milk relating to the region of the Brazil that the breastfeeding lives, the proximity to an industry, the proximity to a contaminated river or sea, the type of milk (colostrum, foremilk and hindmilk) and the number of past pregnancies

Correlation of quantitative sensorial descriptors and chromatographic signals of beer using multivariate calibration strategies.

In this study, two important sensorial parameters of beer quality ? bitterness and grain taste ? were correlated with data obtained after headspace solid phase microextraction ? gas chromatography with mass spectrometric detection (HS-SPME?GC?MS) analysis. Sensorial descriptors of 32 samples of Pilsner beers from different brands were previously estimated by conventional quantitative descriptive analyses (QDA). Areas of 54 compounds systematically found in the HS-SPME-GC?MS chromatograms were used as input data. Multivariate calibration models were established between the chromatographic areas and the sensorial parameters. The peaks (compounds) relevant to build each multivariate calibration model were determined by genetic algorithm (GA) and ordered predictors selection (OPS), tools for variable selection. GA selected 11 and 15 chromatographic peak areas, for bitterness and grain taste, respectively; while OPS selected 17 and 16 compounds for the same parameters. It could be noticed that seven variables were commonly pointed out by both variable selection methods to bitterness parameter and 10 variables were commonly selected to grain taste attribute. The peak areas most significant to the evaluation of the parameters found by both variable selection methods fed to the PLS algorithm to find the proper models. The obtained models estimated the sensorial descriptors with good accuracy and precision, showing that the utilised approaches were efficient in finding the evaluated correlations. Certainly, the combination of proper chemometric methodologies and instrumental data can be used as a potential tool for sensorial evaluation of foods and beverages, allowing for fast and secure replication of parameters usually measured by trained panellists

Study of analytical techniques to determine chlorpyrifos in the surface waterways of the rural zone of Ouro Branco, Brazil : a case study.

The presence of insecticides in the waterways of the municipality of Ouro Branco, MG, Southeastern Brazil, has become a public health problem. Recent research correlates the presence of these toxins in the water to the high indexes for hypertension and abortions occurring in the rural area. These insecticide residues are only slightly concentrated in the water, and as such, it is necessary to search for and optimize analytic methods that are capable of detecting these very low concentrations. To define the method that presents the best detectability for the organochlorine chlorpyrifos, one of the most used pesticides in the area, sample extraction techniques such as liquid?liquid extraction with low temperature partition (LLE-LTP) and headspace solid-phase microextraction (HS-SPME) were used, followed by gas chromatography analysis with electron capture detection (GC-ECD). Full factorial design 24 and the Doehlert matrix were used to optimize both extraction techniques. The results displayed that HS-SPME-GCECD was the method that presented the best performance for determining the presence of chlorpyrifos in the water. The optimum condition was defined at the extraction time and temperature of 60 min and 85 ?C, respectively, with a sample volume of 11 mL and Na2HPO4 concentration of 0.04 mol/L. The optimized method was validated for the principal figures of merit. The method displayed linearity with R2 equal to 0.992 and detection limit (LOD) and quantification limit (LOQ) of 0.50 and 1.67 ?g/L, respectively. The results indicate that the HS-SPME-GC-ECD technique proposed is efficient for determining the presence of chlorpyrifos in water, and analyses of the collected sample indicated the presence of chlorpyrifos in water bodies in the rural zone of Ouro Branco in concentrations within detection and quantification limits

Polymeric microparticles for modified release of NPK in agricultural applications.

The aim of this work was to synthesize polymeric microparticles as carriers for nitrogen, phosphorus, and potassium (NPK fertilizer) for agricultural applications, using polyglycerol (PG) to improve the synthesis procedure. Multivariate experimental designs were employed to obtain a satisfactory synthesis. The desirability function identified the best conditions for preparation of the microparticles as being 100.00 mg of poly(e-caprolactone) (PCL), 825.00 mg of PG, 9.25 mL of chloroform, and 0.9% w/v of polyvinyl alcohol (PVA). This resulted in average encapsulation rates of 94.23% for N, 99.80% for P, and 65.00% for K. The profile of release from the microparticles was according to diffusion following Fick?s Law. These observations confirmed the capacity of the proposed microparticles to sustain a continuous and prolonged release of NPK for the purpose of plant fertilization

Effects of Fe(III) and quality of humic substances on As(V) distribution in freshwater : use of ultrafiltration and Kohonen neural network.

Humic substances (HS) are ubiquitous organic compounds able to affect mobility and availability of arsenic (As) in aquatic systems. Although it is known that associations between HS and As occur mainly via iron (Fe)-cationic bridges, the behaviour and distribution of this metalloid in HS- and Fe-rich environments is still not fully understood. In this paper, the quality of HS from different rivers in Brazil and Germany and its influence on the behaviour of As(V) under different Fe(III) concentrations were investigated. HS were extracted from four different rivers (Cascatinha, Holtemme, Selke and Warme Bode), characterised and fractionated into different molecular weight sizes (10, 5 and 1 kDa). Complexation tests were performed using an ultrafiltration system and 1 kDa membranes. All data was analysed using the Kohonen neural network (SOM e Self organising maps). All samples, except Selke, exhibited similar results of free As (<1 kDa). The results suggested that associations between HS, Fe and As were dependent on nitrogen (N)earomatic carbon (C), amount of sulphur (S) and the molecular size of the HS. Although all HS appeared to be similar after looking at most variables analysed, the SOM could discriminate them into three different groups. Characterisation of the HS indicated that they had terrestrial material (from C3 plants) as precursor material. Most of the As and Fe was distributed in the fractions of higher (>10 kDa) and lower (<1 kDa) size. HS quality is an important factor to take into account when studying the behaviour of As in HS-rich environments

Method development for simultaneous determination of polar and nonpolar pesticides in surface water by low-temperature partitioning extraction (LTPE) followed by HPLC-ESI-MS/MS.

During this research, chemometric approaches were applied for optimization of the low-temperature partitioning extraction (LTPE) for the simultaneous analysis of the pesticides: acephate, difenoconazole, fenamidone, fluazifop, fluazinam, methamidophos, and thiamethoxam from surface water samples and determination by high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry. It was used the 23 full factorial and the Doehlert experimental designs. The extraction technique was optimized by evaluating the effects of the three variables: sample pH, ionic strength (addition of Na2HPO4), and organic solvent volume. Considering the interest to find an optimal condition for all analytes simultaneously, the best extraction parameters found were as follows: pH = 5.33, concentration of Na2HPO4 = 0.0088 mol L?1 and organic phase volume = 4.5 mL. The optimized methodology showed LOD and LOQ levels from 0.33 to 8.13 ng L?1 and from 1.09 to 26.84 ng L?1, respectively. The recovery values ranged from 38.37 and 99.83% and the RSD values varied from 2.33 to 18.92%. The method was applied to surface water analysis sampled in areas with intensive agricultural practices in Ouro Branco City, Minas Gerais, Brazil. The difenoconazole was detected in concentrations between 12.53 and 94.76 ng L?1